冗余链路技术在工业以太网中的应用分析

结合以太网的通信机制，通过冗余链路技术来提高工业以太网通信网络系统的可靠性和容错能力，重点分析了通过生成树协议或快速生成树协议解决冗余链路技术所产生的交换环路问题，及冗余链路技术在工业以太网中的应用。     

基于关联分析的高校校园网告警系统

随着高校校区的扩大,网络规模越来越大,结构也趋于复杂、异构,这就需要对网络进行有效的管理以维持其可靠性和可用性.告警相关性分析作为网络故障管理中的重要内容,有助于处理冗余告警、定位故障及预防故障的发生.提出使用关联规则分析的告警系统,这些规则可以作为先验知识来指导网络智能化故障定位、诊断和预测.     

基于时间、空间和规则的无线网络告警关联方法

无线网络应用的普及使网络故障管理成为网络运维的关键。为了从大量的网络告警数据中快速准确地找到告警、定位根源故障,提出了一种基于时间、空间和规则的无线网络告警关联方法。该方法基于专家规则库、网络拓扑结构,以及告警的时间序列,将时间、空间、传统单一的规则告警关联方法相结合,综合定位根源告警;针对大型复杂网络结构,采用层次关联的方法, 针对大型复杂网络结构,采用层次关联的方法,先找到产生告警的子网,再从该子网的节点间找到产生告警的节点;同时,通过动态维护网络拓扑结构和专家规则库,能够适应无线网络的动态变化特点。实验结果表明,提出的基于时间、空间和规则的告警关联方法的准确率为86.6%。     

一种基于相关度统计的告警事件关联算法

网络故障与网络告警事件之间存在着必然的关系.在分析故障传播和告警模式的基础上,将同一故障引发的告警事件区别为根源事件和并发事件,通过对隐藏在海量事件中的根源事件与并发事件相关度的统计,发现每一类故障的相关事件类集合,并以此作为关联规则,进行事件关联处理,能够正确地进行故障定位,有效过滤冗余事件,为故障原因分析提供依据.     

一种改进型FNG状态机的研究及实现

为了提高网络故障管理系统的效率,结合原有CFM技术,采用特殊故障屏蔽机制和告警优先级别机制相结合的综合性告警管理策略的FNG状态机,其设置了故障生命期限和故障再现期限值,增加了打印告警信息前的告警优先级别判断。在运行时,杜绝了用户界面的刷屏现象并能打印出三种级别的告警信息。测试结果表明,改进型FNG状态机可以突显高级别告警,单次显示重复性故障的告警,不显示短暂性故障的告警,从而减少了网络管理员定位故障的劳动量,提高了工作效率。     

一种以太网链路质量检测的方法

随着网络技术的不断发展和网络规模的不断扩大,对网络运维管理能力提出了更高的要求。Y.1731协议是一个以太网OAM标准,可以用来提供以太网的运维、管理和维护功能。在此重点讲述了该协议在以太网链路层中如何提供端到端的链路连通性检测、故障定位及链路质量的监控,包括链路丢包检测和链路时延检测,并进行了终端配套软件的研究和设计。     

基于关联规则的网络故障告警相关性分析

针对网络故障复杂、告警数据库信息量大等问题,提出一种改进的增量式关联规则挖掘算法。采用关联规则挖掘技术,对告警数据库中的更新规则进行增量关联规则挖掘,将传统告警分析方法与挖掘出的关联规则相结合,应用于网络故障告警相关性分析中。实验结果表明,该算法能减少冗余规则,提高挖掘效率。     

高可用性工业以太网技术的研究与实现

针对现场总线控制系统对高可靠性和稳定性的要求会限制工业以太网的应用问题,在原有网络的基础上添加一个并行的冗余链路,以增强通信的可用性,减小通信链路故障对系统运行的影响。结合EPA通信协议栈,设计并行网络冗余解决方案,包括协议栈结构、帧结构和通信过程。给出该方案的性能分析。     

千兆冗余以太网链路状态检测技术

针对千兆以太网冗余备份网卡的链路切换时间较长,难以满足应用要求的问题,提出一种基于“心跳”帧的新型千兆冗余以太}回链路状态检测方法。通过改进MAC控制器,增加网络状态检测模块用于组织“心跳”帧,并根据检测网络延时对以太网络进行分类,得到Delay网络和No—Delay网络,利用“心跳”帧对冗余链路的实时监控以及MAC控制器对“心跳”帧的统计分析,快速检测出断开链路,缩短链路切换时间。测试结果表明,该硬件检测方法能够迅速检测出断开链路,并在20ms之内完成冗余链路切换,保障网络通信的可靠性和稳定性。     

网络故障管理告警关联技术分析

随着通信网络的迅速发展和业务的拓展,对网络的管理与维护变得越来越困难。告警相关性在通信网络故障维护中起着十分关键的作用。文中首先介绍了故障管理和告警关联的概念,在此基础上介绍了几种告警关联方法,分析了它们的优缺点。通过对比几种方法,提出采用基于事例推理和基于规则推理的两种方法对故障进行关联分析。     

Case-base maintenance with multi-objective evolutionary algorithms

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.     

Reference architecture for robot teleoperation:: development details and practical use

The need to avoid redundant efforts in software development has been recognized for a long time. Currently, work is focused on the generation of products that are designed to be reused. A reference architecture for robot teleoperation systems has been developed using the domain-engineering process and certain architectural patterns. The architecture has been applied successfully for the development of different teleoperation platforms used in the maintenance activities of nuclear power plants. In particular, this paper presents how the reference architecture has been implemented in different systems, such as the Remotely Operated Service Arm (ROSA), the Teleoperated and Robotized System for Maintenance Operation in Nuclear Power Plants Vessels (TRON) and the Inspection Retrieving Vehicle (IRV).     

一种基于相似粗糙集技术的案例库维护

最近的研究工作突现了在案例推理过程中案例库维护的重要性,越来越多的人认为基于案例推理系统包含了案例库维护的有关过程(Review和Restore)。案例库维护作为CBR研究的一个分支,已经研究出不同的案例库维护策略,其中一些是限制案例库的规模,由此引发了CBR系统的能力与效率问题。相似粗糙集技术可以有效地利用差别矩阵,通过不同的相似度阈值发现以及处理案例库的冗余,有选择地删除多余的案例;同时案例库的覆盖度不降低,减少了案例适应性修改的代价,从而确保了CBR系统的能力与效率的兼顾。     

高速公路机电维护管理信息系统的构建

构建以设备属性、日常维护记录为基础数据的高速公路机电维护管理信息系统,能有效提供一个积累数据、分析数据的科学平台,有效提高日常维护的预见性和计划性,建立以预防性养护为主的科学养护体系。     

基于虚拟维修的CAD-VR建模及优化技术研究

虚拟维修样机建模是开发虚拟维修训练系统的首要和关键性问题。本文分析了基于虚拟维修的CAD-VR模型的建模思路和建模要求,分别从几何建模、物理建模、模型转换、模型优化等方面探讨了某型导弹战车建模及模型优化关键技术,所建立的样机模型已成功在某型导弹战车虚拟维修训练系统中得到了应用。     

基于MTN的维修工作建模与仿真技术研究

维修工作过程模型是维修性与维修工作分析与评定的重要基础和手段,现有的建模技术在描述维修过程以及维修过程的相关信息等方面不够直观、也不够丰富。基于Petri网的建模概念,提出了一种用于描述维修工作过程的建模技术——维修工作网（Maintenance Task Net,MTN）;详细叙述了通过维修工作组成元素和维修工作过程关系描述维修工作过程的方法;提出了针对不同维修工作的初始信息进行维修工作建模的两种建模方式;基于该建模技术,开发了软件工具在维修工作分析、虚拟维修仿真中进行了应用研究,验证了MTN的实用性。     

Modeling risk based maintenance using fuzzy analytic network process

The study deals with the problem of maintenance policy selection for an industrial unit. Maintenance policy selection is a multiple criteria decision making problem. Criteria considered here are ‘risk of equipment failure’ and the ‘cost of maintenance’. The maintenance policies considered are Corrective Maintenance (CM), Time Based Maintenance (TBM), Condition Based Maintenance (CBM) and Shutdown Maintenance (SM). For modeling, fuzzy analytic network process (FANP) has been employed. Chang’s extended analysis has been applied to deal with the fuzzy variables and the preferred maintenance policy alternative is found out using FANP analysis. The methodology was applied to a unit of a chemical plant and the suitable maintenance policy was found out for each of the 13 equipment of the unit. The results were compared to the earlier study using Analytic Hierarchal Process and Goal Programming (Arunraj and Maiti, 2010) vis-a-vis the existing practices. The results show that CBM is preferred when the risk possessed by an equipment is very high while CM is preferred in those cases where risk is low and cost is the main consideration. But in cases where both cost and risk are somewhat equally important, TBM is the preferred option.     

医学院校《计算机组装与维护》课程教学探索

《计算机组装与维护》是一门技能性课程,是一门理论与实践密切结合,并侧重操作技术和应用能力培养的课程。文章从该课程实际的教学现状出发,结合我校学生特点,以及自己教学的经验和体会,提出了若干切实可行的改革建议。     

基于参数化动作描述的虚拟维修过程仿真

在虚拟人执行维修动作的过程中,参数化动作描述起到了虚拟人动作和自 然语言维修指令之间的桥梁作用,是为了规范虚拟人执行维修任务动作。论文在参数化动作 描述和智能虚拟维修训练环境的基础上,构建了维修任务分解模型和动作库;以参数化动作 描述为基础,提出了用于3D 虚拟人维修动作仿真的虚拟维修实时执行层次结构。最后,通 过某型起落架维修任务对参数化动作和仿真架构可行性进行验证,结果表明,基于参数化动 作描述描述的动作库可以很方便的进行维修过程仿真。     

Redundant representations in evolutionary computation

This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2(kr)/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.